Online System Application Peer Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online systems face challenges in providing relevant performance metrics for applications due to differences in user audiences and interaction types, making it difficult for entities to evaluate application performance effectively.
Innovation Solution
The online system selects additional applications with a threshold measure of similarity to the target application based on genres, user characteristics, and interaction metrics, generating scores and providing metrics or information about these applications to help entities evaluate performance relative to relevant peers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the online system provides metrics for additional applications with different user audiences and interaction types, then the quantity of available comparison data increases, but the relevance and usefulness of the metrics for evaluating application performance decreases
Solution Approach 1:
The system applies local quality by selecting additional applications based on specific similarity criteria (genre, user demographics, interaction types) rather than providing all available applications. This ensures that the comparison data provided is locally optimized for relevance to the target application, resolving the contradiction between quantity and relevance by filtering for quality in specific dimensions.
Solution Approach 2:
The system changes parameters by introducing multiple similarity dimensions (genre, user demographics, interaction types) to select additional applications. By adjusting these selection parameters, the system provides a controlled quantity of highly relevant comparison data, balancing quantity and relevance through parameter-based filtering.
2Measurement precision
If the online system selects additional applications based on multiple similarity criteria (genre, user characteristics, interaction metrics), then the relevance of comparison data improves, but the complexity of the selection process increases
Solution Approach 1:
The system segments the selection process into distinct criteria (genre matching, user demographic similarity, interaction type compatibility). By dividing the complex selection task into separate, manageable segments, the system achieves high relevance through multiple dimensions while keeping each selection criterion independently implementable and manageable.
Solution Approach 2:
The system applies universality by using a multi-functional selection framework that evaluates applications across multiple dimensions (genre, users, interactions). This universal approach allows the same selection mechanism to handle diverse comparison needs while maintaining relevance, reducing overall complexity through a unified multi-criteria process.
3Loss of information
If the online system provides detailed metrics and scores for multiple additional applications, then the information completeness for evaluation improves, but the information overload and difficulty in identifying relevant comparisons increases
Solution Approach 1:
The system implements feedback by providing scores that quantify the similarity between the target application and additional applications. This feedback mechanism helps entities prioritize which comparisons are most relevant, reducing information overload by highlighting the most important comparisons while maintaining complete metric data for thorough evaluation.
Solution Approach 2:
The system extracts and highlights the most relevant comparison data by providing scored rankings of additional applications. By extracting the key similarity metrics and presenting them in a prioritized format, the system maintains information completeness while making it easier to identify and focus on the most relevant comparisons.
Data Source
AI summary
An online system maintains information describing interactions by its users with various applications. To allow evaluation of an application against other applications, the online system identifies additional applications having a threshold measure of similarity to the application and with which at least at threshold number of users interacted during a time interval. Based on a number of users who interacted with various additional applications and amounts of revenue obtained by additional applications, the online system selects a group of additional applications. The online system selects additional applications from the group based on scores for the additional applications determined from user interaction and revenue obtained by the additional applications and provides information about the additional applications selected from the group to an entity associated with the application.